EP1699345B1 - Systeme et methode permettant d'optimiser des prescriptions optiques cliniques - Google Patents

Systeme et methode permettant d'optimiser des prescriptions optiques cliniques Download PDF

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EP1699345B1
EP1699345B1 EP04813941A EP04813941A EP1699345B1 EP 1699345 B1 EP1699345 B1 EP 1699345B1 EP 04813941 A EP04813941 A EP 04813941A EP 04813941 A EP04813941 A EP 04813941A EP 1699345 B1 EP1699345 B1 EP 1699345B1
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prescription
psf
patient
pupil
metric
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EP1699345A2 (fr
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Larry N. Thibos
Raymond A. Applegate
Arthur Bradley
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Indiana University Research and Technology Corp
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/0016—Operational features thereof
    • A61B3/0025—Operational features thereof characterised by electronic signal processing, e.g. eye models
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present invention relates to calculating the quality of vision and optical prescriptions for correcting optical imperfections in a patient's eyes.
  • Clinical optical prescriptions are calculated to determine the best combination of spherical and cylindrical lenses that optimize visual acuity for distant objects, thereby maximizing the quality of the retinal image.
  • optimizing a prescription in the presence of high order aberrations as defined by many of the optic metrics used today does not always result in a prescription that causes the best focus as determined by a patient. Therefore, once an optic prescription for spherical and cylindrical correction is calculated, a clinician typically must use an iterative method for fine tuning that calculation in a trial and error fashion, as the patient compares the effect on vision while the clinician changes between slightly different corrective lenses. Since this method is time consuming, subjective, and requires clinical experience to practice accurately and efficiently, a method for objectively calculating an optimal clinical prescription without an iterative trial and error process is desired.
  • a clinician in determining an optimal optical prescription, a clinician must also take into account the so-called refractionist's rule "maximum plus to best visual acuity.” This requires a clinician to calculate a prescription wherein the spherical component of a myopic (nearsighted) eye is slightly under-corrected and a hyperopic (farsighted) eye is slightly overcorrected.
  • the calculation for the ideal optical focus is not necessarily the best clinical prescription, requiring a second calculation or iterative testing to determine the optimal optic prescription. Because iterative calculations or testing is time consuming, a method is needed for incorporating the "maximum plus to best visual acuity" rule into a method for optimizing clinical optic prescriptions.
  • Modem clinical aberrometers e.g., Shack-Hartmann wavefront-sensing technique or subjective ray-tracing technique
  • Such aberration maps have the potential to accurately form the basis of wavefront-guided corrections of the eye (e.g., refractive surgery, IOLs, and contact lens design.
  • detailed aberration maps contain a wealth of information that can overwhelm a clinician and impede interpretation.
  • One strategy to simplify an aberration measurement into a metric of image quality includes the use of the second order aberrations of astigmatism and defocus in an aberration map to prescribe a correcting lens that eliminates second-order aberrations.
  • eliminating the second-order Zemike aberrations may not optimize either the subjective impression of best-focus or the objective measurement of visual performance. This phenomenon may be explained by the lack of a universally-accepted metric of image quality to establish an objective optimum-focus state for an aberrated eye.
  • a useful metric of optical quality for the human eye is one that is highly correlated with visual performance, the subjective judgment of best focus, or other tasks that are optically limited.
  • a system for prescribing and evaluating clinical optic prescriptions should employ metrics that are good objective indicators of subjective visual acuity for aberrated retinal images.
  • the Guirao Patent describes an iterative method for finding the optimum sphere, cylinder and axis parameters by simultaneously optimizing a metric of image quality.
  • this method requires a great deal of computing power to perform, and may not optimize the visual quality, due to the metric employed.
  • this method does not incorporate the many clinical considerations into a method for determining an optic prescription that best suits a patient, and this patent is confined to the use of a small subset optical quality metrics to determine a refraction. Therefore, a method allowing multiple uses of multiple metrics of vision quality would be appreciated, greatly increasing the flexibility and utility of automated refraction and assessment of the visual consequences of different refractive treatments.
  • US2002/0167643 discloses a method for providing an objective manifest refraction of a patient's eye.
  • US6,511,180 discloses determination of ocular refraction from wavefront aberration data and design of an optimum customised correction.
  • an eye-care practitioner must take into consideration patient data, environmental data, and apply clinical judgment to adjust the optically optimized prescription. Failure to include these inputs can cause numerous unacceptable prescriptions.
  • Each of these steps often requires subjective decisions based upon clinical experience, and must be made after or in addition to the calculations for optical optimizations based upon aberration measurements. Therefore, a method or system incorporating these clinical and patient considerations is desired. Further, an automated method or system to perform this task in a more accurate fashion utilizing less computing power is desired.
  • the present invention relates to the quantification of visual quality, differential diagnosis of optical dysfunction, and the optimization of optical corrections to improve the quality of life of the individual patient.
  • a method of evaluating the optical quality and optical health of the eye, using this knowledge to provide an optical prescription is based on visual quality metrics combined with knowledge of the patient, optical needs, and the many clinical considerations that lead to improved quality of life derived from providing an customized optical correction.
  • a method is provided as claimed in claim 1.
  • the method may comprise an additional step wherein the calculated ideal optic prescription is adjusted to maximize depth of focus for a particular use or distance.
  • the method may be performed using a computer microprocessor.
  • Vision quality metrics have many uses in eye care. They are used to determine if a patient's vision is sub-normal, to discriminate between optical and neurological causes of reduced vision, to select appropriate methods of treatment, to monitor outcome of that treatment, and to optimize vision by additional follow-up treatments. When combined with additional information about previous health history, lifestyle, and vision needs, the ultimate aim is improved quality of life through improved optical quality of the patient's eyes.
  • FIG. 2 a system and method for calculating a refractive prescription for an individual's needs, a flow chart for data input and integration for calculation is shown.
  • the path to the final prescription represents one embodiment of the present invention.
  • Fig. 1 shows the prior art system of calculating an optically optimized refractive prescription from aberrometry data alone, optimized for a specified working distance.
  • This prior art system is incomplete, as it requires additional clinical trial and error to obtain a clinically acceptable prescription, such as the classic eye care professional iteratively asking the patient to compare a first and second lens to optimize a refractive prescription for one particular mode of prescription (e.g. contact lenses, multifocal lenses, IOL's, glasses, etc.).
  • clinical considerations and optometric and ophthalmic practice indicate that, in addition to the aberrometric data 110, metric selection 120, patient data 130, environmental data 140, and clinical considerations 160 result is a more complete system that makes more accurate judgments. These judgments are more complete, as they rely upon on multiple inputs and a feedback loop as illustrated in Fig. 2 , as opposed to simply using aberrometric optimizations and then relying upon iterative trial and error used in the prior art systems as shown in Fig. 1 .
  • the present invention optimizes the metrics through the use of a feedback loop wherein the patient is allowed to participate in assessing the relative merits of each of the modes of refractive correction.
  • the present invention calculates an optimized refractive prescription for multiple modes of correction (each of which mode of prescription is automatically optimized without the use of iterative subjective input), a patient may be allowed to assess the relative merits of each of the modes of prescription of the refractive correction (e.g. whether the patient would prefer multifocal lenses, contact lenses, IOL's, etc.) through computer modeling.
  • an embodiment of a method to optimize optic prescriptions may take the form of multiple steps.
  • the steps would include obtaining aberrometry data 110 such as the vector of Zernike coefficients (or any other representation) defining an aberration map, pupil radius, wavelength of light employed in the aberrometer, and other appropriate data typically collected by an aberrometer.
  • aberrometry data 110 such as the vector of Zernike coefficients (or any other representation) defining an aberration map, pupil radius, wavelength of light employed in the aberrometer, and other appropriate data typically collected by an aberrometer.
  • the method suggests selecting a metric for expressing visual quality 120, which can take the form of several metrics employed in the art today, including those detailed below.
  • patient data is obtained 130, such as the age of the patient, the patient's habitual prescription, and the type of correction contemplated (glasses, contact lenses, refractive surgery, inlays, etc.).
  • Additional environmental data may be obtained 140, such as the manner in which the patient typically uses her eyes, the type of working environment the patient typically finds herself in, and the current temperature and humidity under which the aberrometry data is taken and the correction applied.
  • clinical considerations and nomograms may be entered for consideration, such as how quickly the clinician wishes to adjust the current prescription, and whether the clinician wishes to adjust the prescription to implement the refractionist's rule of maximum plus to best visual acuity.
  • a calculation of an ideal prescription is made 150, using the selected metric/s to determine an objective best focus suitable for the visual task of interest, and that prescription is adjusted 160 to take into account the clinical considerations for optimizing subjective best focus, thereby leading to optic prescription 170.
  • aberrometric data is typically collected through a commercial aberrometer.
  • the data collected by an aberrometer typically measures wavefront slope at multiple locations within the pupil for specific wavelength. These slopes can be used to reconstruct a monochromatic wavefront error map or using models of chromatic aberration to estimate the polychromatic wavefront errors of the eye. These error maps can be represented by a spectra of Zernike or Fourier coefficients.
  • This data is typically collected from an aberrometer that employs a Shack-Hartmann wavefront-sensing technique or an objective or subjective ray tracing technique that provides detailed maps of the eye's refracting properties across the entire pupil. From this data, an aberration map can be formed by methods well known in the art, and the data can be stored on a connected computer to be used for calculating an ideal optic prescription according to calculations or software programs described in further detail below.
  • methods for determining an ideal optic refractive prescription for an eye are based upon optimizing visual quality by one of the two following methods: (1) a curve fitting method designed to find the nearest spherocylindrical approximation to the actual wavefront aberration map, and (2) a metric optimization through virtual through focus in which various amounts of spherical or cylindrical wavefront is added to or subtracted from an aberration map until the optical quality of the eye is maximized.
  • a curve fitting method designed to find the nearest spherocylindrical approximation to the actual wavefront aberration map
  • a metric optimization through virtual through focus in which various amounts of spherical or cylindrical wavefront is added to or subtracted from an aberration map until the optical quality of the eye is maximized.
  • a first method for calculating an ideal optic prescription is equivalent quadratic fitting.
  • Curvature is the property of wavefronts that determines focus.
  • the sphero-cylindrical equivalent of an aberration map is that quadratic (i.e. a sphero-cylindrical) surface which best represents the map.
  • quadratic i.e. a sphero-cylindrical
  • This idea of representing an aberrated map with a quadratic surface is an extension of the ophthalmic technique of representing a quadratic surface with an "equivalent sphere.”
  • a reasonable way to fit an arbitrary wavefront with a quadratic surface is to match the curvature of the two surfaces at some reference point.
  • the surface sought in one aspect of this invention is the osculating quadric.
  • a closed-form solution exists for the problem of deriving the power vector coordinates of the correcting lens from the Zernike coefficients of the wavefront by computing the Zernike expansion of the Seidel formulae for defocus and astigmatism.
  • Another way to determine the focus error of an eye, and thereby calculate an ideal prescription is to move an object axially along the line-of-sight until the retinal image of that object appears subjectively to be well focused.
  • This procedure can be simulated mathematically by adding a spherical wavefront to the eye's aberration map and then computing the retinal image using methods of Fourier optics as further described by Thibos, Applegate, and Bradley in “Accuracy and Precision of Objective Refraction from Wavefront Aberrations," Journal of Vision (2003 ) (herein incorporated by reference).
  • the curvature of the added wavefront can be systematically varied to simulate a through-focus experiment that varies the optical quality of the eye and lens system over a range from good to bad. Given a suitable metric of optical quality, this computational procedure yields the optimum power M of the spherical correcting lens needed to maximize optical quality of the corrected eye. With this virtual spherical lens in place, the process can be repeated for "through-astigmatism" calculations to determine the optimum values of J0 and J45 needed to maximize image quality. With these virtual astigmatic lenses in place, the last step would be to fine-tune the determination of M by repeating the through-focus calculations. Optionally, a computer simulation of the Jackson Cross-cylinder test for axis and power of astigmatism could also be performed.
  • this method is not limited to optimize only spherical and astigmatic aberrations, but may be used to optimize a myriad of other refractive aberrations. Regardless of the aberration optimized through this method, the method shall be described as a "through focus" experiment herein.
  • This computational method captures the essence of refraction by successive elimination and refinement used clinically by mathematically simulating the effect of sphero-cylindrical lenses of various powers. That quadratic wavefront which maximizes the eye's optical quality when added to the eye's aberration map defines the ideal correcting lens, eliminating or reducing the need for clinical trial and error.
  • Figs. 3A and 3B display intermediate results for optimizing the pupil fraction metric PFWc (the metric described in further detail below).
  • PFWc the pupil fraction metric described in further detail below.
  • a curve is generated relating RMS wavefront error to pupil radius.
  • Each of these curves crosses the criterion level ( ⁇ /4 in these calculations) at some radius value. That radius is interpreted as the critical radius since it is the largest radius for which the eye's optical quality is reasonably good.
  • the set of critical radius values can then be plotted as a function of defocus, as shown in Fig. 3B .
  • calculating an ideal optic prescription in many cases first involves the selection of a metric that expresses overall image quality so that computation and interpretation can be more readily managed.
  • a clinician In selecting a metric of optical quality for the human eye, a clinician typically wants to select a metric that is highly correlated with visual performance.
  • a vast number of metrics are currently used in the optic and optometric arts, and the best metric for use in optimizing a patients optic prescription has not yet been proven or established in the fields of optometry and ophthalmology. Therefore, one embodiment of the present invention anticipates the flexibility of utilizing any selected metric for expressing overall image quality.
  • a perfect optical system has a flat wavefront aberration map and therefore metrics of wavefront quality are designed to capture the idea of flatness. Since a wavefront, its slope, and its curvature each admits to a different optical interpretation, meaningful scalar metrics based on all three are herein provided: the wavefront aberration map, the slope map, and the curvature map.
  • Wavefront error describes optical path differences across the pupil that give rise to phase errors for light entering the eye through different parts of the pupil.
  • Two common metrics of wavefront flatness follow.
  • RMS w root - mean - squared wavefront error computed over the whole pupil microns
  • RMS W is the standard deviation of the values of wavefront error specified at various pupil locations.
  • PV peak - to - valley difference microns
  • PV max w x y - min w x y PV is the difference between the highest and lowest points in the aberration map.
  • Wavefront slope is a vector-valued function of pupil position that requires two maps for display. Wavefront slopes may be interpreted as transverse ray aberrations that blur the image.
  • the root-mean-squared value of a slope map is a measure of the spreading of light rays that blur the image in one direction.
  • the total RMS value computed for both slope maps taken together is thus a convenient metric of wavefront quality that may be interpreted in terms of the size of the spot diagram.
  • RMS s root - mean - squared wavefront slope computed over the whole pupil arcmin
  • RMS s 1 A ⁇ ⁇ pupil ⁇ w x x y - w ⁇ x 2 + w y x y - w ⁇ y 2 d xdy 0.5
  • w x dw / dx
  • w y dw / dy are the partial spatial derivatives (i.e. slopes) of w(x,y)
  • A pupil area.
  • Wavefront curvature describes focusing, errors that blur the image. To form a good image at some finite distance, wavefront curvature must be the same everywhere across the pupil. A perfectly flat wavefront will have zero curvature everywhere, which corresponds to the formation of a perfect image at infinity. Like wavefront slope, wavefront curvature is a vector-valued function of position that requires more than one map for display. The principal curvatures at every point can be derived from maps of mean curvature M(x,y) and Gaussian curvature G(x,y) as follows.
  • the Gaussian and mean curvature maps may be obtained from the spatial derivatives of the wavefront aberration map using textbook formulas.
  • M the spherical equivalent
  • J 0 the normal component of astigmatism
  • J 45 the oblique component of astigmatism
  • the length of the power vector which is the definition of blur strength
  • a map of the length of the power-vector representation of a wavefront at each point in the pupil may be called a blur-strength map.
  • the blur-strength map To compute the blur-strength map first use the principal curvature maps to compute the astigmatism map.
  • J x y k 1 x y - k 2 x y 2 and then combine the astigmatism map with the mean curvature map using the Pythagorean formula to produce a blur strength map.
  • B x y M 2 x y + J 2 x y
  • the spatial average of this blur strength map is a scalar value that represents the average amount of focusing error in the system that is responsible for image degradation,
  • Metrics of wavefront quality can be defined based on the concept of pupil fraction.
  • Pupil fraction is defined as the fraction of the pupil area for which the optical quality of the eye is reasonably good. A large pupil fraction is desirable because it means that most of the light entering the eye will contribute to a good-quality retinal image.
  • Pupil Fraction Area of good pupil Total area of pupil
  • critical pupil or central pupil
  • the second general method for determining the area of the good pupil is called the "tessellation" or “whole pupil” method.
  • a perfect optical system images a point object into a compact, high-contrast retinal image.
  • the image of a point object is called a point-spread function (PSF).
  • the apodization function is usually omitted.
  • the waveguide nature of cones must be taken into account. These waveguide properties cause the cones to be more sensitive to light entering the middle of the pupil than to light entering at the margin of the pupil. It is common practice to model this phenomenon as an apodizing filter with transmission A(x,y) in the pupil plane.
  • Scalar metrics of image quality that capture the quality of the PSF in aberrated eyes are designed to capture the dual attributes of compactness and contrast.
  • the first 5 metrics listed below measure spatial compactness and in every case small values of the metric indicate a compact PSF of good quality.
  • the last 6 metrics measure contrast and in every case large values of the metric indicate a high-contrast PSF of good quality.
  • Most of the metrics are completely optical in character, but a few also include knowledge of the neural component of the visual system.
  • Several of these metrics are 2-dimensional extension of textbook metrics defined for 1-dimensional impulse response functions. Many of the metrics are normalized by diffraction-limited values and therefore are unitless.
  • D ⁇ 50 diameter of a circular area centered on PSF peak which captures 50 % of the light energy arcmin
  • EW equivalent width of centered PSF arcmin
  • the equivalent width of the PSF is the diameter of the circular base of that right cylinder which has the same volume as the PSF and the same height.
  • EW 4 ⁇ ⁇ pupil ⁇ PSF x y d xdy ⁇ PSF x 0 y 0 0.5
  • x 0 , y 0 are the coordinates of the peak of the PSF.
  • x,y are spatial coordinates of the retinal image, typically specified as visual angles subtended at the eye's nodal point. Note that although EW describes spatial compactness, it is computed from PSF contrast. As the height falls the width must increase to maintain a constant volume under the PSF.
  • SM square root of second moment of light distribution arcmin
  • CW correlation width of ligth distribution arcmin
  • PSF ⁇ PSF is the autocorrelation of the PSF.
  • SRX Strehl ratio computed in spatial domain
  • the domain of integration is the central core of a diffraction-limited PSF for the same pupil diameter.
  • STD standard deviation of intensity values in the PSF , normalized to diffraction - limited value
  • This metric measures the variability of intensities at various points in the PSF
  • STD ⁇ psf ⁇ PSF N x y - PSF ⁇ 2 d xdy 0.5 ⁇ psf ⁇ PSF DL x y - PSF ⁇ DL 2 d xdy 0.5
  • PSF DL is the diffraction-limited point-spread function.
  • the domain of integration is a circular area centered on the PSF peak and large enough in diameter to capture most of the light in the PSF.
  • ENT entropy of the PSF This metric is inspired by an information-theory approach to optics.
  • This metric was introduced by Williams as a way to capture the effectiveness of a PSF for stimulating the neural portion of the visual system. This is achieved by weighting the PSF with a spatial sensitivity function that represents the neural visual system and then integrating the result over the domain of the PSF.
  • NS ⁇ psf ⁇ PSF x y ⁇ g x y d xdy ⁇ psf ⁇ PSF DL x y ⁇ g x y d xdy
  • g(x,y) is a bivariate-Gaussian, neural weighting-function.
  • a profile of this weighting function shows that it effectively ignores light outside of the central 4 arc minutes of the PSF.
  • VSX visual Strehl ratio computed in the spatial domain.
  • the visual Strehl ratio weights the PSF with a neural weighting function before computing the Strehl ratio.
  • the difference between NS and VSX is in the choice of weighting functions.
  • VSX ⁇ psf ⁇ PSF x y ⁇ N x y d xdy ⁇ psf ⁇ PSF DL x y ⁇ N x y d xdy
  • N(x,y) is a bivariate neural weighting function equal to the inverse Fourier transform of the neural contrast sensitivity function for interference fringes.
  • any object can be conceived as the sum of gratings of various spatial frequencies, contrasts, phases and orientations.
  • the optical system of the eye is considered a filter that lowers the contrast and changes the relative position of each grating in the object spectrum as it forms a degraded retinal image.
  • a high-quality OTF is therefore indicated by high MTF values and low PTF values.
  • Scalar metrics of image quality in the frequency domain are based on these two attributes of the OTF.
  • SFcMTF spatial frequency cutoff of radially - averaged modulation - transfer function rMTF
  • Cutoff SF is defined here as the intersection of the radially averaged MTF (rMTF) and the neural contrast threshold function.
  • the rMTF is computed by integrating the full 2-dimensional MTF over orientation. This metric does not capture spatial phase errors in the image because rMTF is not affected by the PTF portion of the OTF.
  • SFcMTF highest spatial freq. for which rMTF > neural threshold
  • rMTF f 1 2 ⁇ ⁇ ⁇ 0 2 ⁇ ⁇ abs OTF f ⁇ d ⁇
  • OTF(f, ⁇ ) is the optical transfer function for spatial frequency coordinates/(frequency) and (orientation).
  • SFcOTF cutoff spatial frequency of radially-averaged optical-transfer function (rOTF).
  • the radially-averaged OTF is determined by integrating the full 2-dimensional OTF over orientation. Since the PTF component of the OTF is taken into account when computing rOTF, this metric is intended to capture spatial phase errors in the image.
  • SFcOTF lowest spatial freq. for which rOTF ⁇ neural threshold
  • rOTF f 1 2 ⁇ ⁇ ⁇ 0 2 ⁇ ⁇ OTF f ⁇ d ⁇
  • OTF(f, ⁇ ) is the optical transfer function for spatial frequency coordinates/(frequency) and ⁇ (orientation). Since the OTF is a complex-valued function, integration is performed separately for the real and imaginary components. Conjugate symmetry of the OTF ensures that the imaginary component vanishes, leaving a real-valued result.
  • SFcMTF ignores phase errors, with phase-altered and even phase-reversed modulations treated the same as correct-phase modulations.
  • the SFcMTF identifies the highest frequency for which modulation exceeds threshold, irrespective of lower frequency modulation minima and phase reversals.
  • SFcOTF identifies the highest SF within the correct-phase, low-frequency portion of the OTF. This allows "spurious resolution" to be discounted when predicting visual performance on tasks of spatial resolution and pattern discrimination.
  • AreaMTF area of visibility for rMTF normalized to diffraction - limited case .
  • the area of visibility in this context is the region lying below the radially averaged MTF and above the neural contrast threshold function.
  • T N is the neural contrast threshold function, which equals the inverse of the neural contrast sensitivity function.
  • AreaOTF area of visibility for rOTF (normalized to diffraction-limited case).
  • the area of visibility in this context is the region that lies below the radially averaged OTF and above the neural contrast threshold function.
  • SRMTF Strehl ratio computed in frequency domain MTF method
  • the Strehl ratio computed by the OTF method will accurately compute the ratio of heights of the PSF and a diffraction-limited PSF at the coordinate origin.
  • the peak of the PSF does not necessarily occur at the coordinate origin established by the pupil function. Consequently, the value of SROTF is not expected to equal SRX exactly, except in those special cases where the peak of the PSF occurs at the coordinate origin.
  • modulation in spatial frequencies above the visual cut-off of about 60 c/deg is ignored, and modulation near the peak of the CSF (e.g.
  • VSMTF visual Strehl ratio computed in frequency domain OTF method
  • This metric differs from VSX by emphasizing image quality at the coordinate origin, rather than at the peak of the PSF.
  • VOTF volume under OTF normalized by the volume under MTF
  • This metric is intended to quantify phase shifts in the image. It does so by comparing the volume under the OTF to the volume under the MTF.
  • VOTF ⁇ - ⁇ ⁇ ⁇ - ⁇ ⁇ OTF f x f y ⁇ df x ⁇ df y ⁇ - ⁇ ⁇ ⁇ - ⁇ ⁇ MTF f x f y ⁇ df x ⁇ df y Since the MTF ⁇ the real part of the OTF, this ratio is always ⁇ 1. Creation of this metric was inspired by a measure of orientation bias of the receptive fields of retinal ganglion cells.
  • VNOTF volume under neurally - weighted OTF , normalized by the volume under neurally - weighted MTF
  • This metric is intended to quantify the visually-significant phase shifts in the image. It does so by weighting the MTF and OTF by the neural contrast sensitivity function before comparing the volume under the OTF to the volume under the MTF.
  • VNOTF ⁇ - ⁇ ⁇ ⁇ - ⁇ ⁇ CSF f x f y ⁇ OTF f x f y ⁇ df x ⁇ df y ⁇ - ⁇ ⁇ ⁇ - ⁇ ⁇ CSF f x f y ⁇ MTF f x f y ⁇ df x ⁇ df y
  • the wavefront aberration function is a monochromatic concept. If a source emits polychromatic light, then wavefront aberration maps for each wavelength are treated separately because lights of different wavelengths are mutually incoherent and do not interfere. For this reason, metrics of wavefront quality do not generalize easily to the case of polychromatic light. This lack of generality is a major limitation of the wavefront metric approach to quantifying the optical quality of an eye.
  • PSF poly may be substituted for PSF in any of the equations given above to produce new, polychromatic metrics of image quality.
  • other metrics can be devised to capture the changes in color appearance of the image caused by ocular aberrations.
  • the chromaticity coordinates of a point source may be compared to the chromaticity coordinates of each point in the retinal PSF and metrics devised to summarize the differences between image chromaticity and object chromaticity. Such metrics may prove useful in the study of color vision.
  • a polychromatic optical transfer function OTF poly may be computed as the Fourier transform of PSF poly . Substituting this new function for OTF (and its magnitude for MTF) in any of the equations given above will produce new metrics of polychromatic image quality defined in the frequency domain.
  • an optimized prescription that is clinically preferable must include such considerations as depth of focus at distance and the refractionist's rule "maximum plus to best visual acuity,” should consider the prior prescription used by the patient, should consider how the patient plans to use the prescription, and may take into account other clinical considerations such as different prescriptions for the two eyes of the same patient.
  • adjusting an ideal prescription to maximize the usable depth of focus is typically performed by adjusting the spherical lens power of a prescription so that the power is slightly less negative (in the case of myopia) or slightly more positive (in the case of hyperopia) than the lens required to make the prescription the optical ideal.
  • a myopic patient will typically have her prescription adjusted to be 0.2-0.5 diopters less negative than the ideal prescription
  • a hyperopic patient will typically have her prescription adjusted to be 0.2 to 0.5 diopters more positive than her ideal prescription.
  • the method of performing method may include the additional step of eliminating the bulk of the spherical and cylindrical refractive errors by nulling the second-order Zemike coefficients or nulling the second-order Seidel coefficients in the aberrometric map. From this higher-order aberration map, optimization of the selected metrics can be performed. If the metric to be optimized is the root-mean-square metric, the prescription can be computed directly from the value of the second-order Zemike coefficient. If the clinician chooses to optimize the metric using the equivalent quadratic fitting method described above, the prescription can be computed directly from the values of the second-order and fourth-order Zemike coefficients.
  • these through-focus calculations are performed by selecting a value of one of the second-order coefficients from a pre-selected set of values that correspond to clinically meaningful (e.g. 1/8 Diopter) steps of focus (Z2 ⁇ 0), orthoastigmatsm (Z2 ⁇ +2), or oblique astigmatism (Z2 ⁇ -2).
  • the prescription then, is the sum of optimum values obtained and the original second order aberration coefficients nulled to create the higher order aberration map.
  • the computer calculations are not constrained to any specific increments of power (e.g. 1/8 diopter) and may, in fact, use much finer steps to improve resolution.
  • the results for discrete steps may be interpolated by suitable computer algortithms to achieve very high resolution.
  • the embodiments of the present invention provide a method for objectively calculating an optimal subjective clinical prescription with little iterative trial and error, as well as utilizing a method that allows a clinician to optimize an optical prescription using several metrics of optical quality. Further, it will be appreciated that the embodiments of the present invention provide a means for adjusting an objective calculation to take into account maximized depth of focus, as well as other clinical information such as patient data and environmental data.
  • the patient can also quantify the optical corrections that will achieve any desired depth of focus by manipulating the eye+lens higher order aberrations using the same virtual through focus methods. This would be particularly useful for the correction of presbyopia (fixed focus eye).
  • presbyopia fixed focus eye
  • the embodiments of the present invention provide a method for optimizing a clinical prescription that requires less computing power than previous methods and provide improved flexibility over prior art methods. This approach not only provides reduced computational requirements, but also vastly improved flexibility in that it can be used to do more than just optimize sphero-cylindrical prescriptions, but also the depth of focus, and as such enable automated refractions for a range of levels of emerging presbyopia and visual environments.

Claims (12)

  1. Procédé permettant d'optimiser une prescription réfractive, le procédé comprenant les étapes consistant à :
    a. obtenir des données aberrométriques d'un patient par le biais d'un aberromètre ; et
    b. utiliser les données aberrométriques pour réaliser un calcul d'ajustement quadratique équivalent pour calculer la surface sphéro-cylindrique qui représente le mieux la carte d'aberration qui maximise la qualité de l'image rétinienne, pour obtenir au moins une prescription sphéro-cylindrique réfractive clinique pour le patient, moyennant quoi le procédé pour optimiser une prescription réfractive est réalisé sans utiliser d'entrée subjective itérative.
  2. Procédé selon la revendication 1, comprenant en outre l'étape consistant à ajuster la prescription réfractive, pour maximiser l'utilisation de la profondeur de champ d'un patient si les données aberrométriques suggèrent que le patient est myope.
  3. Procédé selon la revendication 1, comprenant en outre l'étape consistant à ajuster la prescription optique idéale pour maximiser l'utilisation de la profondeur de champ du patient si les données aberrométriques suggèrent que la vision est hypermétrope.
  4. Procédé selon l'une quelconque des revendications précédentes, dans lequel l'étape consistant à utiliser les données aberrométriques pour réaliser un calcul d'ajustement quadratique équivalent est réalisée par un processeur informatique.
  5. Procédé selon l'une quelconque des revendications précédentes, comprenant en outre l'étape consistant à évaluer les résultats et à permettre à un utilisateur de déterminer si la prescription devrait être ultérieurement optimisée.
  6. Procédé selon l'une quelconque des revendications précédentes, comprenant en outre l'étape consistant à sélectionner une parmi une pluralité de prescriptions optiques.
  7. Procédé selon l'une quelconque des revendications précédentes, comprenant en outre les étapes consistant à :
    a. obtenir des données du patient ; et
    b. utiliser les données du patient pour optimiser une prescription réfractive clinique.
  8. Procédé selon l'une quelconque des revendications précédentes, comprenant en outre les étapes consistant à :
    a. obtenir des données environnementales ; et
    b. utiliser les données du patient pour optimiser une prescription réfractive clinique.
  9. Procédé selon l'une quelconque des revendications précédentes, dans lequel le point de repère pour effectuer le calcul d'ajustement quadratique équivalent est le centre de la pupille.
  10. Procédé selon l'une quelconque des revendications précédentes, dans lequel la surface quadratique osculatrice est calculée.
  11. Procédé selon l'une quelconque des revendications précédentes, dans lequel le calcul d'ajustement quadratique équivalent est réalisé en utilisant l'expansion de Zernike de la formule de Seidel pour le flou et l'astigmatisme, pour obtenir les coordonnées du vecteur de puissance de la lentille correctrice.
  12. Procédé selon la revendication 11, dans lequel le calcul utilise les résultats des équations : M = - c 2 0 ⁢ 4 ⁢ 3 + c 4 0 ⁢ 12 ⁢ 5 - c 6 0 ⁢ 24 ⁢ 7 + ⋯ r 2
    Figure imgb0072
     J 0 = - c 2 2 ⁢ 2 ⁢ 6 + c 4 2 ⁢ 6 ⁢ 10 - c 6 2 ⁢ 12 ⁢ 14 + ⋯ r 2
    Figure imgb0073
     J 45 = - c 2 - 2 ⁢ 2 ⁢ 6 + c 4 - 2 ⁢ 6 ⁢ 10 - c 6 - 2 ⁢ 12 ⁢ 14 + ⋯ r 2
    Figure imgb0074
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EP1699345A4 (fr) 2008-06-11
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